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4 articles for “local search techniques”
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Hybrid Evolutionary Algorithm for Automated Test Case Generation in Large-Scale Software Systems
Abstract: Automated test case generation is a crucial activity for ensuring the reliability, robustness, and quality of large-scale software systems. Conventional test generation techniques often face significant challenges in handling scalability, achieving high code coverage, and adapting to complex and heterogeneous software architectures. Evolutionary algorithms have emerged as effective solutions, capable of exploring large search spaces and generating test suites with improved coverage and fault detection capabilities. However, individual evolutionary strategies, …
Published in International Journal of Software Computing and Testing · Vol. 12, Issue 1, 2026 · pp. 15–21 Read article
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A Review of Video Compression Technique of an Efficient Rate-Distortion
Abstract: The digital video compression technology has been gaining popularity for many years. Today, when people enjoy HDTV (high-definition television), movie broadcasting through the Internet, or the digital music, such as MP3, the convenience that the digital video industry brings to us cannot be forgotten. All of these should attribute to the advances in compression technology, enhancement on mass storage media, or streaming video/audio services. Most rate-distortion optimized division way of …
Published in International Journal of Digital Communication and Analog Signals · Vol. 4, Issue 2, 2018 · pp. 1–8 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A Hybrid Metaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 35–46 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A HybridMetaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 Read article